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Mean–Variance portfolio selection in presence of infrequently traded stocks

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  • Castellano, Rosella
  • Cerqueti, Roy

Abstract

This paper deals with a mean–variance optimal portfolio selection problem in presence of risky assets characterized by low-frequency trading and, therefore, low liquidity. To model the dynamics of illiquid assets, we introduce pure-jump processes. This leads to the development of a portfolio selection model in a mixed discrete/continuous time setting. We pursue the twofold scope of analyzing and comparing either long-term investment strategies as well as short-term trading rules. The theoretical model is analyzed by applying extensive Monte Carlo experiments, in order to provide useful insights from a financial perspective.

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  • Castellano, Rosella & Cerqueti, Roy, 2014. "Mean–Variance portfolio selection in presence of infrequently traded stocks," European Journal of Operational Research, Elsevier, vol. 234(2), pages 442-449.
    • Handle: RePEc:eee:ejores:v:234:y:2014:i:2:p:442-449
    DOI: 10.1016/j.ejor.2013.04.024
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    Cited by:

    1. Masoud Fekri & Babak Barazandeh, 2019. "Designing an Optimal Portfolio for Iran's Stock Market with Genetic Algorithm using Neural Network Prediction of Risk and Return Stocks," Papers 1903.06632, arXiv.org.
    2. Eduard Gabriel Ceptureanu & Sebastian Ceptureanu & Claudiu Herteliu, 2021. "Evidence regarding external financing in manufacturing MSEs using partial least squares regression," Annals of Operations Research, Springer, vol. 299(1), pages 1189-1202, April.
    3. Bodnar, Taras & Parolya, Nestor & Schmid, Wolfgang, 2018. "Estimation of the global minimum variance portfolio in high dimensions," European Journal of Operational Research, Elsevier, vol. 266(1), pages 371-390.
    4. Huang, Xiaoxia & Ma, Di & Choe, Kwang-Il, 2023. "Uncertain mean–variance portfolio model with inflation taking linear uncertainty distributions," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 203-217.
    5. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    6. Castellano, Rosella & Cerqueti, Roy & Spinesi, Luca, 2016. "Sustainable management of fossil fuels: A dynamic stochastic optimization approach with jump-diffusion," European Journal of Operational Research, Elsevier, vol. 255(1), pages 288-297.
    7. Maurizio Bovi & Roy Cerqueti, 2016. "Forecasting macroeconomic fundamentals in economic crises," Annals of Operations Research, Springer, vol. 247(2), pages 451-469, December.
    8. Ha, Youngmin & Zhang, Hai, 2020. "Algorithmic trading for online portfolio selection under limited market liquidity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1033-1051.
    9. Zinoviy Landsman & Udi Makov & Tomer Shushi, 2018. "A Generalized Measure for the Optimal Portfolio Selection Problem and its Explicit Solution," Risks, MDPI, vol. 6(1), pages 1-15, March.

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